A Hybrid Early-Warning System for Inflation in an Emerging Market: Combining Econometric Models, an Agent-Based Decomposition with Heterogeneous Expectations, a Large Language Model, and a Multi-Output Agent Architecture

We develop and evaluate a hybrid early-warning system for year-over-year (YoY) inflation in Colombia that combines four econometric models (ARIMA, LASSO, ElasticNet, and a weighted ensemble), a reduced-form VAR, an agent-based model with heterogeneous expectations and tradable/non-tradable pass-through (ABM v2), a large language model (LLM) forecaster, and a multi-output agent architecture, integrated through Dynamic Model Averaging (DMA). We evaluate the system on a rolling out-of-sample backtest from February 2010 to March 2026 (n ≈ 194 months) spanning the 2021–2023 inflation surge and its ongoing disinflation. Five contributions emerge. First, an identity-based monthly-to-YoY decomposition applied uniformly across reduced-form models reduces MAE by 15–20% relative to direct YoY forecasting without adding variables. Second, regime-conditional analysis shows that MAE is 2.0–3.0 times larger in surge regimes than in stable regimes across all models. Third, an ABM with regime-dependent heterogeneous expectations reduces full-sample MAE from 0.337 pp (v1) to 0.268 pp (v2, −20.4%) and surge-regime MAE from 0.585 pp to 0.404 pp (−31.0%), with Diebold-Mariano statistic 6.21 (p